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Sparsity-aware model reduction for post-layout circuit simulation

机译:用于布局后电路仿真的稀疏感知模型简化

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Post-layout simulation becomes more and more challenging due to ever increasing parasitics. Reducing the order of systems does not necessarily result in improvement in simulation time. What really affects the simulation time is the network complexity, which is determined by both the size and density of the network. Only reducing the size may worsen the density and in turn the complexity of the “reduced” system may be even higher compared with the original systems. We thus proposed a model complexity reduction which comprises sparsity-aware model reduction followed by model sparsification. Experiments show that for realistic post-layout simulation problems, the proposed model complexity reduction provides 3X~5X speed-ups compared with simulation using the original circuits.
机译:由于寄生效应的不断增加,布局后仿真变得越来越具有挑战性。减少系统的顺序并不一定会导致仿真时间的缩短。真正影响仿真时间的是网络复杂度,它由网络的大小和密度决定。仅减小尺寸可能会使密度变差,并且与原始系统相比,“精简”系统的复杂度甚至可能更高。因此,我们提出了一种模型复杂度降低方法,其中包括稀疏感知模型降低和模型稀疏化。实验表明,对于实际的布局后仿真问题,与使用原始电路进行仿真相比,所提出的模型复杂度降低可将速度提高3倍至5倍。

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